{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "matched-billy",
   "metadata": {},
   "source": [
    "# 麦叔编程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "important-module",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "provincial-sample",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n"
     ]
    }
   ],
   "source": [
    "for i in range(8):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "motivated-membership",
   "metadata": {},
   "outputs": [],
   "source": [
    "1. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "pretty-dollar",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (README.en.md, line 7)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"/Users/zjueman/git/data_analysis/README.en.md\"\u001b[0;36m, line \u001b[0;32m7\u001b[0m\n\u001b[0;31m    Software architecture description\u001b[0m\n\u001b[0m             ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "%run "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "infectious-garden",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbbd9884340>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt.plot(np.random.randn(50).cumsum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "foreign-crowd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
